Learning of fish movement pattern by neural network

Yoshiteru Takezawa, Hidekazu Suzuki, Mamoru Minami, Yasushi Mae

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

This paper presents a prediction method of fish movement based on learning its movement by using the Neural Network. The position of a fish is obtained by model-based matching and gazing-GA in real-time. The back-propagation is used for learning and predicting the movement of fish, where fish position is used as the input/teaching signal. The architecture and the internal parameter of the Neural Network are determined by basic experiments which use the simulated movement of fish. Experimental results using a swimming fish show that the proposed method can predict the movement of fish.

Original languageEnglish
Pages2400-2405
Number of pages6
Publication statusPublished - Dec 1 2005
Externally publishedYes
EventSICE Annual Conference 2005 - Okayama, Japan
Duration: Aug 8 2005Aug 10 2005

Other

OtherSICE Annual Conference 2005
CountryJapan
CityOkayama
Period8/8/058/10/05

Keywords

  • Back-propagation
  • Gazing-GA
  • Neural Network
  • Visual Sarvoing

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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  • Cite this

    Takezawa, Y., Suzuki, H., Minami, M., & Mae, Y. (2005). Learning of fish movement pattern by neural network. 2400-2405. Paper presented at SICE Annual Conference 2005, Okayama, Japan.